Fast federated machine unlearning with nonlinear functional theory
Federated machine unlearning (FMU) aims to remove the influence of a specified subset of
training data upon request from a trained federated learning model. Despite achieving …
training data upon request from a trained federated learning model. Despite achieving …
Accelerated federated learning with decoupled adaptive optimization
The federated learning (FL) framework enables edge clients to collaboratively learn a
shared inference model while kee** privacy of training data on clients. Recently, many …
shared inference model while kee** privacy of training data on clients. Recently, many …
Fedasmu: Efficient asynchronous federated learning with dynamic staleness-aware model update
As a promising approach to deal with distributed data, Federated Learning (FL) achieves
major advancements in recent years. FL enables collaborative model training by exploiting …
major advancements in recent years. FL enables collaborative model training by exploiting …
Expressive 1-lipschitz neural networks for robust multiple graph learning against adversarial attacks
Recent findings have shown multiple graph learning models, such as graph classification
and graph matching, are highly vulnerable to adversarial attacks, ie small input …
and graph matching, are highly vulnerable to adversarial attacks, ie small input …
Federated fingerprint learning with heterogeneous architectures
Recent studies on federated learning (FL) have sought to solve the system heterogeneity
issue by designing customized local models for different clients. However, public dataset …
issue by designing customized local models for different clients. However, public dataset …
Aedfl: efficient asynchronous decentralized federated learning with heterogeneous devices
Federated Learning (FL) has achieved significant achievements recently, enabling
collaborative model training on distributed data over edge devices. Iterative gradient or …
collaborative model training on distributed data over edge devices. Iterative gradient or …
Integrated defense for resilient graph matching
A recent study has shown that graph matching models are vulnerable to adversarial
manipulation of their input which is intended to cause a mismatching. Nevertheless, there is …
manipulation of their input which is intended to cause a mismatching. Nevertheless, there is …
Unsupervised adversarial network alignment with reinforcement learning
Network alignment, which aims at learning a matching between the same entities across
multiple information networks, often suffers challenges from feature inconsistency, high …
multiple information networks, often suffers challenges from feature inconsistency, high …
Adversarial attack against cross-lingual knowledge graph alignment
Recent literatures have shown that knowledge graph (KG) learning models are highly
vulnerable to adversarial attacks. However, there is still a paucity of vulnerability analyses of …
vulnerable to adversarial attacks. However, there is still a paucity of vulnerability analyses of …
Robust network alignment via attack signal scaling and adversarial perturbation elimination
Recent studies have shown that graph learning models are highly vulnerable to adversarial
attacks, and network alignment methods are no exception. How to enhance the robustness …
attacks, and network alignment methods are no exception. How to enhance the robustness …